OOI Biogeochemical Sensor Data: Best Practices and User Guide. Version 1.0.0.

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users seeking to work with OOI biogeochemical (BGC) sensor data, our Working Group and Beta Testers realized that the processing required to meet the specific needs of all end users across a wide range of potential scientific applications and combinations of OOI BGC data from different sensors and platforms couldn’t be synthesized into a single “recipe”. We therefore provide here the background information and principles needed for the end user to successfully identify and understand all the available “ingredients” (data), the types of “cooking” (end user processing) that are recommended to prepare them, and a few sample “recipes” (worked examples) to support end users in developing their own “recipes” consistent with the best practices presented here. This is not intended to be an exhaustive guide to each of these sensors, but rather a synthesis of the key information to support OOI BGC sensor data users in preparing science-ready data products. In instances when more in-depth information might be helpful, references and links have been provided both within each chapter and in the Appendix

TALKING SCIENCE – BRIDGING THE GAP BETWEEN SCIENTIST AND JOURNALIST

participantThe present public debate about climate change and the challenges it poses to society call for comprehensive science communication. The pitfalls of miscommunication are widely recognized as it has become clear that the public's understanding and interpretation of science plays a pivotal role in the perception of issues that span both science and policy. Scientists naturally feel a responsibility to communicate their knowledge and research findings to an audience beyond the scientific community itself, yet their efforts don't always yield the desired effect, leading to frustration and – in some cases – even withdrawal. Having worked as both a scientist and a science journalist, I would like to share insights from both perspectives that may help improve the communication between researchers and reporters. Rather than place these two professions on opposing sides, I would like to show how they could work together more efficiently. After all, they are working towards a common goal. A deeper understanding of the other's obligations, limitations, demands and strengths will make the process more pleasant and produce better results

Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales

The presence of algal pigments in sea ice alters under-ice irradiance spectra, and the relationship between these variables can be used as a non-invasive means for estimating ice- associated algal biomass on ecologically relevant spatial and temporal scales. While the influence of snow cover and ice algal biomass on spectra transmitted through the snow-ice matrix has been examined for the Arctic, it has not been tested for Antarctic sea ice at regional scales. We used paired measurements of sea ice core chla concentrations and hyperspectral-transmitted under-ice irradiances from 59 sites sampled off East Antarctica and in the Weddell Sea to develop algorithms for estimating algal biomass in Antarctic pack ice. We compared 4 approaches that have been used in various bio-optical studies for marine systems: normalised difference indices, ratios of spectral irradiance, scaled band area and empirical orthogonal functions. The percentage of vari- ance explained by these models ranged from 38 to 79%, with the best-performing approach being normalised difference indices. Given the low concentrations of integrated chl a observed in our study compared with previous studies, our statistical models performed surprisingly well in explaining variability in these concentrations. Our findings provide a basis for future work to develop methods for non-invasive time series measurements and medium- to large-scale spatial mapping of Antarctic ice algal biomass using instrumented underwater vehicles

BGC-Argo quality control manual for nitrate concentration

This document is the Argo quality control (QC) manual for nitrate concentration, where the metadata parameter name for the state variable is NITRATE with units μmol kg-1. The document describes two levels of quality control: - The first level is the “real-time” (RT) quality control system, which always includes a set of agreed-upon automatic quality-control tests on each measurement. Data adjustments can also be applied within the real-time system, and quality flags assigned accordingly. - The second level is the “delayed-mode” (DM) quality control system where data quality is assessed in detail by a delayed-mode operator and adjustments (based on comparison to high-quality reference fields) are derived. As mentioned, these adjustments can then be propagated forward and applied to incoming data in real-time until the next delayed-mode assessment is performed

Diel quenching of Southern Ocean phytoplankton fluorescence is related to iron limitation

Evaluation of photosynthetic competency in time and space is critical for better estimates and models of oceanic primary productivity. This is especially true for areas where the lack of iron (Fe) limits phytoplankton productivity, such as the Southern Ocean. Assessment of photosynthetic competency on large scales remains challenging, but phytoplankton chlorophyll a fluorescence (ChlF) is a signal that holds promise in this respect as it is affected by, and consequently provides information about, the photosynthetic efficiency of the organism. A second process affecting the ChlF signal is heat dissipation of absorbed light energy, referred to as non-photochemical quenching (NPQ). NPQ is triggered when excess energy is absorbed, i.e. when more light is absorbed than can be used directly for photosynthetic carbon fixation. The effect of NPQ on the ChlF signal complicates its interpretation in terms of photosynthetic efficiency, and therefore most approaches relating ChlF parameters to photosynthetic efficiency seek to minimize the influence of NPQ by working under conditions of sub-saturating irradiance. Here, we propose that NPQ itself holds potential as an easily acquired optical signal indicative of phytoplankton physiological state with respect to Fe limitation. We present data from a research voyage to the Subantarctic Zone south of Australia. Incubation experiments confirmed that resident phytoplankton were Fe-limited, as the maximum quantum yield of primary photochemistry, F-v/F-m, measured with a fast repetition rate fluorometer (FRRf), increased significantly with Fe addition. The NPQ "capacity" of the phytoplankton also showed sensitivity to Fe addition, decreasing with increased Fe availability, confirming previous work. The fortuitous presence of a remnant warm-core eddy in the vicinity of the study area allowed comparison of fluorescence behaviour between two distinct water masses, with the colder water showing significantly lower F-v/F-m than the warmer eddy waters, suggesting a difference in Fe limitation status between the two water masses. Again, NPQ capacity measured with the FRRf mirrored the behaviour observed in F-v/F-m, decreasing as F-v/F-m increased in the warmer water mass. We also analysed the diel quenching of underway fluorescence measured with a standard fluorometer, such as is frequently used to monitor ambient chlorophyll a concentrations, and found a significant difference in behaviour between the two water masses. This difference was quantified by defining an NPQ parameter akin to the Stern-Volmer parameterization of NPQ, exploiting the fluorescence quenching induced by diel fluctuations in incident irradiance. We propose that monitoring of this novel NPQ parameter may enable assessment of phytoplankton physiological status (related to Fe availability) based on measurements made with standard fluorometers, as ubiquitously used on moorings, ships, floats and gliders

Interacting Effects of Light and Iron Availability on the Coupling of Photosynthetic Electron Transport and CO₂-Assimilation in Marine Phytoplankton

<div><p>Iron availability directly affects photosynthesis and limits phytoplankton growth over vast oceanic regions. For this reason, the availability of iron is a crucial variable to consider in the development of active chlorophyll a fluorescence based estimates of phytoplankton primary productivity. These bio-optical approaches require a conversion factor to derive ecologically-relevant rates of CO₂-assimilation from estimates of electron transport in photosystem II. The required conversion factor varies significantly across phytoplankton taxa and environmental conditions, but little information is available on its response to iron limitation. In this study, we examine the role of iron limitation, and the interacting effects of iron and light availability, on the coupling of photosynthetic electron transport and CO₂-assimilation in marine phytoplankton. Our results show that excess irradiance causes increased decoupling of carbon fixation and electron transport, particularly under iron limiting conditions. We observed that reaction center II specific rates of electron transport (ETR_RCII, mol e- mol RCII^-1 s^-1) increased under iron limitation, and we propose a simple conceptual model for this observation. We also observed a strong correlation between the derived conversion factor and the expression of non-photochemical quenching. Utilizing a dataset from in situ phytoplankton assemblages across a coastal – oceanic transect in the Northeast subarctic Pacific, this relationship was used to predict ETR_RCII: CO₂-assimilation conversion factors and carbon-based primary productivity from FRRF data, without the need for any additional measurements.</p></div

Map of sampling stations along the Line-P transect in the NE subarctic Pacific.

<p>The iron addition experiment was initiated at station P20, located in iron-limited high nutrient low chlorophyll (HNLC) waters. Sampling depths at other stations along the transect were: 30 m at P4; 5 m, 25 m and 40 m at P12, P16, P20 and P26.</p

Time-course of α (a-c) and P_max (d-f) of CO₂-assimilation, ETR_RCII and the derived conversion factor Φ_e:C/n_PSII during the iron addition experiment.

<p>The conversion factor Φ_e:C/n_PSII under light limiting conditions is derived from values in (a) and (b). Similarly, the conversion factor Φ_e:C/n_PSII at light saturation is derived from the values in (d) and (e). The error in (a), (b), (c), and (d) is the 95% confidence interval of the parameter derived from the fit to data from three biological replicates, and the error in (c) and (f) is the propagated error from (a)/(b) and (d)/(e), respectively.</p

Changes in the light dependency of the conversion factor Φ_e:C/n_PSII (a-e) and NPQ_NSV (f-j) over the course of the iron addition experiment.

<p>Units of in Φ_e:C/n_PSII are (mol e- mol C) / (mol chl <i>a</i> mol RCII^<b>-1</b>). The curves were derived by dividing corresponding values of ETR_RCII and CO₂-assimilation from the P<i>vs</i>E curves presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133235#pone.0133235.g003" target="_blank">Fig 3</a>. NPQ was estimated as the normalized Stern-Volmer quenching coefficient NPQ_NSV = F_o′/F_v′ and is unitless [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133235#pone.0133235.ref065" target="_blank">65</a>]. Error bars are the standard error from three biological replicates and often smaller than symbols.</p

Rates of CO₂-assimilation (mol C mol chl <i>a</i>^-1 hr^-1) derived from FRRF measurements plotted against rates measured by ¹⁴C-assimilation experiments.

<p>Samples were taken at one to three depths at five stations along Line-P in the NE subarctic Pacific (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133235#pone.0133235.g001" target="_blank">Fig 1</a>). FRRF based P<i>vs</i>E curves were used to derive ETR_RCII and NPQ_NSV at 8 light levels for each sample, and Φ_e:C/n_PSII values were then derived from the relationship presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0133235#pone.0133235.g007" target="_blank">Fig 7</a>. Φ_e:C/n_PSII and ETR_RCII for each light level were used to calculate CO₂-assimilation rates. Error bars for predicted CO₂-assimilation rates represent the propagated error from the ChlF yields of the last three ST acquisitions of each light level during the FRRF P<i>vs</i>E curve used to derive NPQ_NSV and ETR_RCII. Error bars for measured CO₂-assimilation rates represent the mean coefficient of variance derived from all duplicate measurements (n = 46). The correlation between all predicted and measured data points (n = 95) was statistically significant (Spearman’s <i>r</i> = 0.90, two-tailed p-value < 0.0001). All statistics are for non log-transformed data.</p